Impact of ImageNet pretrain.
For fair comparison to existing unsupervised tracking works, here we train our tracker from scratch.
Detailly, we substitute ResNet50 [1]} pretrained on ImageNet classification as DenseCL [2]} pretrain, which is self-supervised trained with contrastive learning.
Then the trained model is evaluated on OTB2015 and LaSOT benchmark datasets for comparison, results are shown in Table REF .
The performance of the ULAST-on and ULAST-off have a relatively slight performance drop compared to ULAST* model.
It suggests that a better representation can contribute to unsupervised visual tracking.
